Alberto Moya (34)

ABOUT

I am a Ph.D student in Computer Science Department at Universidad de Chile, Chile. My advisor is Prof. Aidan Hogan, and I am a member of the Millennium Institute for Foundational Research on Data.

My general research interests include the Semantic Web, Large-Scale Data Processing and Data Mining. I am particularly interested in designing more advanced approaches to assess the dynamics of SPARQL query results in order to improve the way applications access dynamic Linked Data, making queries more efficient and ensuring fresher results.

RESEARCH

Predicting SPARQL Query Dynamics

The Linked Open Data (LOD) Cloud has grown significantly and a wide variety of applications use information sourced from Linked Datasets to offer services. The SPARQL language is the recommendation for quering Linked Data, but querying SPARQL endpoints has problems such as network latency. Traditionally, caching techniques have been used to deal with the latency problem, but the existing approaches of the traditional Web are not enough, since they do not take into account the special characteristics of LOD. The resources in Linked Data represent real-world things which change over time. The resources described by these datasets are continuously created, moved, deleted, linked, and unlinked. Applications that leverage Linked Data could benefit from knowledge about the dynamics of changing query results to deliver accurate services, since they could keep at least the least dynamic part of the queries. This situation is more critical in the case of applications that consume or interact intensively with Linked Data through SPARQL Query Language, such as query engines and browsers that also constantly receive expensive and repetitive queries. We want to address open questions in terms of assessing the dynamics of SPARQL query results in order to improve the way applications access dynamic Linked Data, making queries more efficient and ensuring fresher results.